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Use when setting up 24/7 background workers. kbot's compound improvement depends on daemons running even when nobody is looking.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Creates distinctive editorial art direction for kernel.chat issues before implementation. Use when choosing an issue's visual concept, typography, composition, cover system, media grammar, responsive transformation, or motion intent; when a page feels generic or lacks character; or when original image and video assets need one coherent visual language.
Audits kernel.chat pages for responsive visual craft, accessibility, motion safety, interaction truth, runtime health, and production rendering. Use when reviewing an issue or artifact, checking whether a design is finished, comparing desktop and mobile, validating reduced motion or print, finding overflow and broken media, or proving that a published route matches the build.
Guides the design, layout, styling, and interactivity of kernel.chat editorial spreads and standalone artifact editions. Use this skill when editing or creating pages, React components, CSS files, or single-file HTML artifacts in the kernel.chat project to enforce the POPEYE-inspired bilingual design grammar and the interaction-language/artifact-language laws.
Use this skill to generate well-branded editorial interfaces and assets for kernel.chat ("Magazine for City Coders" — an independent magazine that is an unnamed homage to POPEYE), either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and editorial UI components for prototyping.
Hand off a model from Blender (via BlenderMCP) into Unity (via MCP for Unity) — export the current Blender model, import it through import_model_file, and place it in the open scene. Use when the user has BlenderMCP and MCP for Unity both connected and wants to bring a Blender model into Unity. Does NOT drive Blender's own generators; BlenderMCP owns how the model got into Blender.
Generate images/videos/3D assets/audio via Higgsfield AI. Defaults: GPT Image 2 for image/design/text, Seedance 2.0 for video, Nano Banana 2/Lite/Pro for character/reference images, Marketing Studio for ads, Seed Audio 1.0 for audio. Use when: "generate an image", "make a video", "animate this photo", "image-to-video", "edit/stylize/remix this image", "reframe this video", "edit this video from a sketch", "create a 3D model/GLB", "create a sound effect", "make music", "text-to-audio", "create an ad", "make a UGC video", "unboxing", "presenter video", "import product from URL", or "analyze video virality". Supports image-to-3D (`multi_image_to_3d`), text-to-audio/music (`seed_audio`), workflow generation (`draw_to_video`, `reframe`), Marketing Studio, and Virality Predictor (`brain_activity`). Chain with higgsfield-soul-id for face/identity consistency. NOT for: Soul Character training (use higgsfield-soul-id), product photoshoots, marketplace listing cards, text/chat/TTS tasks.
| name | daemon-deployment |
| description | Use when setting up 24/7 background workers. kbot's compound improvement depends on daemons running even when nobody is looking. |
| version | 1.0.0 |
| author | kbot |
| license | MIT |
| platforms | ["darwin","linux"] |
| metadata | {"kbot":{"tags":["daemon","launchd","background","24-7","compound"],"related_skills":["autopoiesis-loop","teacher-trace-curation"]}} |
kbot's intelligence doesn't sleep. Three daemons run continuously:
kbot-daemon — code quality, i18n sync, embeddings, docs gaps (every 15 min)kbot-discovery-daemon — self-advocacy, field intelligence (every 15 min → 24 hr cycles)kbot-social-daemon — autonomous posting to X/Bluesky/Mastodon/LinkedIn (daily)Plus a weekly train-self that fine-tunes the local model on curated traces.
VERIFY THE DAEMON IS ACTUALLY RUNNING AFTER INSTALL.
launchd plists that fail silently are the single most common cause of "kbot feels stale" — the daemon was never loaded, so no compound improvement happened.
npm run daemon — run once manually to confirm it works at all.npm run daemon:start — loads the launchd plist.launchctl list | grep kernel.kbot — should show a running entry with PID.tail -f tools/daemon-reports/daemon.log — should show activity within 15 min.npm run daemon:stats — shows task timestamps, token usage, cost savings.If any of steps 3–5 fail, the daemon is not actually running. Debug before moving on.
com.kernel.kbot-daemon.plist → every 15 mincom.kernel.kbot-discovery.plist → every 15 min (internal sub-cycles stagger)com.kernel.kbot-social.plist → daily at 9amcom.kernel.kbot-train-self.plist → Sundays 3amCost: zero. All daemon work routes through local Ollama models.
caffeinate or enable "wake for network access" in Energy Saver if you need guaranteed intervals.localhost:11434. Either start Ollama at login OR the daemon silently no-ops. Add Ollama to Login Items.~/.kbot/ is writable by the daemon user.npm run daemon:stop unloads the plist. Work resumes manually. No state is lost — tools/daemon-reports/state.json persists until the daemon re-enables.
After two weeks of active daemons, the user finds: i18n is always current, the daily digest email is actually read, social posts have engagement, the local model passes a basic task without Claude. The compound output is larger than any single feature could produce.